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Top 5 Technologies Improving Insurance Fraud Detection in 2024

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Top 5 Technologies Improving Insurance Fraud Detection in 2024Top 5 Technologies Improving Insurance Fraud Detection in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

According to the FBI, insurance fraud (excluding health insurance) costs more than $40 billion annually in the U.S. alone. When we add health insurance costs, total insurance costs in the U.S. market exceed $100 billion.

Some of the financial cost of fraud is reflected for policyholders as higher premiums, even though getting the best price for business and property insurance is a priority for customers. Consequently, both insurers and policyholders are negatively impacted by insurance fraud.

Technological tools such as artificial intelligence (AI), internet of things (IoT), and blockchain can be used by insurers to more effectively detect and prevent insurance fraud

In this article we present top 5 insurance fraud detection technologies with their use cases to insurers.

1. Discover policyholder behavioral patterns with Advanced Analytics

Insurers use supervised ML models to identify similarities between previous fraudulent actions. Insurers categorize each case as either fraud or non-fraud, and over time ML models find the parameter values that indicate a suspicious claim and flag them for further investigation.

Unsupervised ML models are also useful for detecting insurance fraud, as fraudsters are always coming up with new fraud schemes. Supervised ML models can be ineffective at detecting new types of fraud, while unsupervised models are good at detecting anomalies.

Behavioral analytics is a new tool that insurers can use to fight insurance fraud. They provide insights into people’s actions by tracking and interpreting their browsing history, clicks, location, etc., and help insurers determine whether or not policyholders’ claims are trustworthy.

Real-life use cases: Advanced and behavioral analytics are implemented to prevent exaggerated claims where fraudsters try to add the cost of previous damages to new claims. Analytics are also effective in preventing false claims where fraudsters change the data since they know that the actual situation is not covered by the insurance policy. For example, the driver could be drunk or the policyholder could commit illegal acts that exclude the insurance company’s liability.

2. Speed up claims processing with Chatbots

NLP-driven customer assistants speed up claims processing. Today, it is possible to submit the first notice of loss (FNOL) by following the instructions of chatbots. Consequently, the first step of claims processing is completed immediately without the need to involve a human expert. Chatbots direct customers to take photos and videos of the damage. This gives potential fraudsters less time to manipulate the data and provide an advantage for insurers to detect fraud.

Real life use case: Thanks to immediate FNOL, fraudsters cannot distort real data. Therefore, the likelihood of false claims reduces.

You can read our article on the Top 3 Insurance Claims Processing Automation Technologies to learn more about claims processing automation.

3. Assess the cost of loss with Computer Vision

Computer vision models infer meanings from visual input such as images and videos. These models can assess the cost of the loss by evaluating videos and photos taken to submit an FNOL. Consequently, the insurance company has an idea of the repair cost of the damage.

Real life use case: Computer vision models help insurers to assess damage data more precisely. Therefore, it prevents inflated repair claims, where fraudsters use false invoices about the maintenance procedure to get more money from the insurance company.

4. Notify claims immediately with IoT

Insurers can be alerted of a claim instantly because of the connected universe of smart devices. For example, in the event of a car accident, the insurance provider will be notified without the policyholder having to contact them. As a result, claims processing begins as soon as the damage occurs, giving fraudsters little time to manipulate data in their favor.

In addition, insurers can use the IoT to compare the policyholder’s information in the FNOL about the location, time, average speed of a car accident, etc., with the data available in the smart vehicle’s memory

Real life use case: Similarly like chatbots, IoT prevents insurers from false claims since fraudsters have less time to change data in their favor.

5. Prevent double dipping fraud with Blockchain

Blockchain is a database network that records transaction data in real time while addressing concerns about security, privacy and control. Therefore, it is beneficial for a number of insurance practices.

Real life use case: Blockchain prevents double dipping, in which insureds file a claim with more than one insurance company. The distributed ledger technology of blockchain could prevent repeated transactions for the same claim from being approved. Only the claim with the most approvals would be regarded valid, while the others would be dismissed.

You might want to check our AI fraud detection article.

You might also want to see our fraud detection software and fraud protection software lists to find tools that improve your fraud prevention. 

Finally, our list of insurance suites can guide you to find insurance fraud detection software.

If you need more information regarding the latest in insurtech solutions or fraud detection we can help.

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
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Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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